Search Results for author: Jinpeng Hu

Found 9 papers, 7 papers with code

A Label-Aware Autoregressive Framework for Cross-Domain NER

1 code implementation Findings (NAACL) 2022 Jinpeng Hu, He Zhao, Dan Guo, Xiang Wan, Tsung-Hui Chang

In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting.

Cross-Domain Named Entity Recognition named-entity-recognition +2

Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation

no code implementations4 Apr 2023 Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, Yue Zhang

To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.

Grammatical Error Correction In-Context Learning +2

Improving Radiology Summarization with Radiograph and Anatomy Prompts

no code implementations15 Oct 2022 Jinpeng Hu, Zhihong Chen, Yang Liu, Xiang Wan, Tsung-Hui Chang

The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists.

Anatomy Contrastive Learning +1

Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training

1 code implementation15 Sep 2022 Zhihong Chen, Yuhao Du, Jinpeng Hu, Yang Liu, Guanbin Li, Xiang Wan, Tsung-Hui Chang

Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training.

Self-Supervised Learning

Graph Enhanced Contrastive Learning for Radiology Findings Summarization

1 code implementation ACL 2022 Jinpeng Hu, Zhuo Li, Zhihong Chen, Zhen Li, Xiang Wan, Tsung-Hui Chang

To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i. e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation.

Contrastive Learning

Word Graph Guided Summarization for Radiology Findings

1 code implementation Findings (ACL) 2021 Jinpeng Hu, Jianling Li, Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan, Tsung-Hui Chang

In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph.

Text Summarization

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